“Our vision was that every single person, when they walked through the door [of a parole hearing], would be scored by a computer” as to his or her risk of recidivism— committing crime again.

Similarly, Oregon launched a crime prediction tool to be consulted by judges when sentencing convicted felons.

The tool predicts the probability that an offender offender will be convicted again for a felony within three years of being released.

The model was developed by analyzing the records of 55,000 Oregon offenders across five years of data.

The model then validated across 350,000 offender records across 30 years of history.

Among the least risky tenth of criminals — those for whom the model outputs the lowest predictive scores— recidivism is just 20 percent.

Yet among the top fifth receiving the highest scores, recidivism will probably occur; over half of these offenders will commit a felony again.

In these “deployments”, predictive analytics “builds upon and expands beyond a longstanding tradition of crime statistics and standard actuarial models” to enable more objective, fact-based decision-making.

But. the stakes are high since miscalculations in the legal arena are more costly than for other applications of predictive analytics.

Lives are literally held in the balance.

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Source: Siegel, Eric (2013-02-07). Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, Wiley. Kindle Edition.